Heikki Handroos is a Full Professor of Mechanical Engineering at LUT University, leading the Laboratory of Intelligent Machines since 1993. He holds a DSc (Technology) from Tampere University of Technology and has served as Vice-Dean of the Faculty of Technology (2007-2009) and currently chairs the Collegiate Body of LUT University. His research focuses on mechatronics, robotics, control systems, and fluid power, with over 300 publications and 2,400+ citations. He has supervised 34 doctoral theses and 150+ MSc projects, managed R&D projects exceeding €20M, and co-founded four tech startups. His work spans industrial collaborations, digital twin applications, and innovative robotics for nuclear energy (e.g., DEMO reactor maintenance systems). He has held visiting professorships in the U.S., Japan, and Russia, and actively contributes to academic editorial roles and professional societies like ASME and IEEE.
Dr. Chenhao Chu is a Professor at ETH Zürich, holding the Professur für Elektronik (Professorship for Electronics). He specializes in RF/mm-Wave circuits, AI-driven design methods, and advanced power amplification technologies. His research focuses on energy-efficient, wideband systems, antenna-in-package solutions, and GaN-based applications for 6G and beyond. Education: Ph.D. in Electronic Engineering, University College Dublin (2022) M.Sc. in Electronic Information Engineering, City University of Hong Kong (2017) Research Interests: His work bridges AI and hardware design, emphasizing reconfigurable circuits , high-linearity power amplifiers , and mm-Wave phased arrays . Key areas include: AI-assisted rapid design synthesis III-V/Si co-design for mm-Wave Efficient antenna integration Dynamic load modulation techniques Awards: Award-winning researcher with distinctions including the First Place Best Student Paper Award (2022 Royal Irish Academy Colloquium) and multiple HEPA-SDC Competition Awards (2021-2022). Recognized for innovations in PA efficiency and design automation. Advising & Grants: Leading projects on 6G PA architectures and AI-driven RF design. Active in IEEE with contributions to conferences like IMS and ARFTG. No explicitly stated grants mentioned but widely cited in industry-academia collaborations. Labs & Teams: Associated with ETH Zürich's Electronics Laboratory, focusing on next-generation wireless systems. Collaborates internationally on 5G/6G infrastructure and mm-Wave innovations.
Elsa A. Olivetti is the Jerry McAfee (1940) Professor in Engineering and Professor of Materials Science and Engineering at MIT, and a MacVicar Faculty Fellow. She leads the Olivetti Group, focusing on sustainable materials design, recycling strategies, and computational models for environmental and economic impact assessment. Her work bridges materials science with sustainability, emphasizing circular economy principles and decarbonization. Education: B.S. in Engineering Science from University of Virginia (2000); Ph.D. in Materials Science and Engineering from MIT (2007). Her doctoral research centered on lithium-ion battery electrode materials. She joined MIT’s Department of Materials Science and Engineering (DMSE) in 2014 as an Assistant Professor, later advancing to full Professor. She co-directs the MIT Climate & Sustainability Consortium and chairs the MIT Climate Nucleus. Research interests include: sustainable materials systems, recycling-friendly material design, waste mining, and AI-driven materials discovery. She develops models for cost prediction, environmental impact analysis, and policy-relevant supply chain dynamics. Notable contributions include high-throughput zeolite design and battery recycling frameworks. Awards include the Bose Teaching Award (2021), NSF Early Career Award (2018), and Minerals, Metals & Materials Society Early Career Fellowship (2019). Her work emphasizes education and curriculum development, including courses for MIT’s Climate Scholars program. Labs/Teams: Olivetti Group (MIT), MIT Climate & Sustainability Consortium. Active in global sustainability initiatives, focusing on materials for energy transition and climate resilience.
Christopher J. Stein is an Associate Professor of Theoretical Chemistry at the Technical University of Munich (TUM), part of the TUM School of Natural Sciences. His research focuses on theoretical (electro-)catalysis, developing electronic-structure models and solvation/embedding methods to understand and optimize catalytic processes. He leads the Stein Group, which integrates computational chemistry with high-throughput simulations to advance energy materials and battery technologies. His work emphasizes realistic modeling of catalyst behavior under operational conditions and has contributed to advancements in quantum embedding and automated reaction mechanism exploration. Education and Career: Earned his PhD in Theoretical Chemistry, with postdoctoral research at Caltech (2017-2020). Became an Associate Professor at TU Munich in 2023. He previously held roles at Karlsruhe Institute of Technology and contributed to projects like the BIG-MAP Materials Acceleration Platform. Research Interests: Theoretical chemistry, electrochemical interfaces, battery materials, high-throughput computational methods, and machine learning integration. His group explores topics like solid electrolyte interphases, charge transfer mechanisms, and automated workflows for materials discovery. Awards: While no explicit awards are listed, his contributions to materials acceleration platforms and theoretical catalysis have been widely recognized in the field. His work has been featured in journals like Journal of Chemical Physics , Chemical Science , and Angewandte Chemie . Labs/Teams: Leads the Stein Group at TUM, collaborating with institutions like the Munich Data Science Institute and MIRMI. His lab focuses on computational tools for accelerating energy material development, including quantum embedding and cloud-based simulations.
Thomas Ouldridge is a Royal Society University Research Fellow and Reader in Biomolecular Systems at the Department of Bioengineering, Faculty of Engineering, Imperial College London. He leads the 'Principles of Biomolecular Systems' group, which focuses on theoretical and computational modeling of complex biochemical systems, particularly exploring the interplay between molecular details and emergent behaviors like sensing, replication, and self-assembly. His work integrates natural systems analysis with synthetic biology applications, aiming to engineer artificial analogs of biological processes. His research spans interdisciplinary areas including stochastic thermodynamics, DNA-based computation, and molecular reaction networks. Key affiliations include the Physics of Life, Synthetic Biology Hub, and the Leverhulme Centre for Cellular Bionics. He has contributed to over 60 peer-reviewed articles since 2009, with recent work emphasizing energy-efficient molecular information processing and thermodynamic limits of biochemical systems. Awards: Royal Society University Research Fellowship (current). Labs/Teams: Principles of Biomolecular Systems Group, collaborating with multiple centers including the Centre for Synthetic Biology and Institute of Chemical Biology. Grants/Positions: Maintains research funding through the Royal Society and UKRI grants, focusing on non-equilibrium biomolecular systems and synthetic biology tools. Recent publications highlight advances in DNA templating networks, stochastic thermodynamic modeling of computation, and optimal protocols for molecular copying systems. His work bridges foundational physics with applied biotechnology, aiming to push the boundaries of synthetic biological engineering.
Dr. Yiran Chen is the John Cocke Distinguished Professor at Duke University's Department of Electrical and Computer Engineering, leading the NSF AI Institute for Edge Computing (Athena) and the Duke Center for Computational Evolutionary Intelligence (DCEI). A global leader in neuromorphic computing, emerging memory systems, and edge AI, he holds prestigious roles including IEEE Fellow and Editor-in-Chief of IEEE Transactions on Circuits and Systems for AI. His research spans machine learning accelerators, security-hardened hardware, and co-design of EDA tools with LLMs. With over 700 publications and 96 patents, he has been awarded 15 paper awards and 17 nominations, including rare Technical Achievement Awards from IEEE societies. He advises over 60 PhD students and 4 postdocs, many of whom hold academic positions worldwide. His work bridges academia and industry, contributing to startups and venture capital through his board roles. Education: B.S. (Tsinghua, 1998) → M.S. (Tsinghua, 2001) → Ph.D. (Purdue, 2005). Career path: Assistant/Associate Professor at University of Pittsburgh (2010–2014) → Duke since 2014. Awards include the ACM SIGDA Outstanding New Faculty Award (2014), NSF CAREER Award (2013), and the Stansell Family Distinguished Research Award (2022). Research focuses on innovations in: (1) Non-volatile memory architectures for AI acceleration, (2) Hardware-software co-design for edge computing, (3) Security in neuromorphic systems, and (4) Large-scale ML for EDA. His group pioneered ReRAM-based accelerators like ReBNN and MARC, and introduced novel edge AI frameworks like Ecco and Prosperity. These works address scalability, energy efficiency, and real-time performance challenges. Key initiatives include the NSF IUCRC for Alternative Sustainable & Intelligent Computing (ASIC), advancing sustainable computing through novel materials and architectures. His leadership in standard-setting bodies like the IEEE Circuits and Systems Society ensures cutting-edge research translates into industry practices. Grants: Lead PIs for multiple NSF AI Institutes and industry partnerships. Labs: Directs the Athena Institute and DCEI, fostering collaboration between academia and industry. Current projects include quantum computing placement algorithms (QPlacer), federated learning frameworks (FedGPT), and neuro-symbolic architectures.
Elisa Santana Monagas is a Part-Time Substitute Professor at the Department of Psychology, Sociology and Social Work, Universidad de Las Palmas de Gran Canaria. She is affiliated with the Institute of Textual Analysis and Applications (IU IAText) and the ICP2 Project research group. Her research focuses on teacher-student communication styles, motivational messages in educational contexts, and their impacts on student motivation, academic performance, and psychological well-being. She employs sentiment analysis and longitudinal studies to explore reciprocal relations between teaching practices and student outcomes, particularly in secondary education settings. Her key research interests include developmental and educational psychology, with an emphasis on autonomy-supportive communication, relatedness in teacher-student relationships, and the role of emotional engagement in learning. She has contributed to understanding how classroom environments influence covitality, boredom reduction, and adaptive behaviors among adolescents. Notable contributions include analyzing reciprocal dynamics between motivational appeals and academic outcomes, gamification strategies in higher education, and the validation of assessment tools for teacher feedback. Her work bridges theoretical frameworks like self-determination theory with methodological innovations in sentiment analysis and needs-supplies fit processes. Elisa collaborates with interdisciplinary teams in the GIR IATEXT group, focusing on didactics and learning in specific educational contexts. She actively publishes in peer-reviewed journals and presents at conferences on topics such as teacher enthusiasm, empathetic communication, and the design of pedagogical interventions to enhance student well-being.
Dr. Michael Stevens is a Senior Lecturer at University of New South Wales (UNSW) Canberra , where he focuses on advanced manufacturing and biomedical device control systems . His work bridges digital manufacturing for SMEs with smart artificial heart technologies , emphasizing industry collaboration and translational research. Specializes in physiological control systems for rotary blood pumps Develops unobtrusive fall detection systems for dementia patients Leads international projects on total artificial heart development Education : B.Eng (Medical - First Class Honours), Queensland University of Technology (2010) PhD in Physiological Control for Biventricular Assist Devices, University of Queensland (2014) Research Trends show consistent focus on: Machine learning for biomedical diagnostics (2018–2025) mmWave radar and thermal sensors in patient monitoring (2021–2024) Computational fluid dynamics in artificial heart modeling (2016–2024) Physiological control algorithms for rotary blood pumps (2011–2025) Scientific Awards : UNSW Scientia Education Award (2021) for contextual teaching Heart Foundation Runner-up for "Smart Artificial Hearts" pitch (2021) ARC PGC Supervisor Award (2017) for mentoring Grants & Supervision : Holds over $6 million in competitive funding including MRFF and ARC grants. Currently supervises 4 PhD students while maintaining industry partnerships with VitalCare and BiVACOR. Labs & Facilities : Works across UNSW Engineering labs and Graduate School of Biomedical Engineering platforms, including mock circulation loops and high-performance computing clusters for CFD simulations.
Paul O'Gorman is a Professor at the Department of Earth, Atmospheric and Planetary Sciences (EAPS) at the Massachusetts Institute of Technology. He currently serves as the Faculty Chair of the EAPS Committee on Education and as the EAPS Graduate Officer. His research focuses on understanding how climate change affects atmospheric circulation and precipitation patterns, particularly extreme events. Education: BA in Theoretical Physics, Trinity College Dublin MSc in High-Performance Computing, Trinity College Dublin PhD in Aeronautics with Minor in Applied Mathematics, California Institute of Technology Research Interests include atmospheric dynamics, hydrological cycle responses to climate change, moist convection, and the application of machine learning to climate modeling. His work addresses regional variability in extreme precipitation, vertical warming profiles in the tropics, and the fluid dynamics of land-ocean warming contrasts. Scientific Awards : Bernhard Haurwitz Memorial Lectureship (2023), American Meteorological Society MIT School of Science Graduate Teaching Prize (2018) Recent Contributions include co-leading the MIT Climate Grand Challenges flagship project "Preparing for a new world of weather and climate extremes" , which develops tools for predicting climate extremes and transitioning to low-carbon resources. He has also explored the asymmetrical generalization capabilities of machine learning algorithms in climate models under warming versus cooling scenarios.
Aonghus Lawlor is an Assistant Professor/Lecturer in Computer Science at the School of Computer Science, University College Dublin. His roles include coordinating modules such as Software Engineering, Data Structures, Machine Learning, and Final Year Project Foundations. He holds an Orcid identifier: 0000-0002-6160-4639. His research focuses on machine learning applications in medical imaging (e.g., MRI, CT), sports science, and healthcare systems. Notable areas include AI-driven diagnostics, cybersecurity in radiology, and genomics for agricultural optimization. Recent work explores ChatGPT4-vision in MS progression, knee osteoarthritis grading via anomaly detection, and reinforcement learning in exercise prescriptions. Professional activities include committee roles in ACM Recommender Systems and Intelligent User Interfaces, grant assessments, and peer reviewing. He has published 137+ outputs, emphasizing interdisciplinary AI solutions with clinical and agricultural impact. Teaching responsibilities span foundational CS courses to advanced ML and project modules. No formal awards are listed, but his work demonstrates contributions to AI ethics, health informatics, and agricultural genomics.
Emanuele Di Lorenzo is a Professor in the Department of Earth, Environmental, and Planetary Sciences at Brown University. Previously, he held roles as Professor and Director (2016–2022) of the Ocean Science and Engineering program at Georgia Tech, which he co-founded. He is the Chairman and co-founder of Ocean Visions, a non-profit transforming academic research into actionable ocean-based climate solutions, and co-leads the United Nations Ocean Decade Collaborative Center on Ocean-Climate Solutions. He earned a Ph.D. in ocean and climate sciences from Scripps Institution of Oceanography (2003) and has been at Brown since 2022. Education: B.S. in Marine Environmental Sciences, University of Bologna (1997) Ph.D. in Climate Sciences, Scripps Institution of Oceanography (2003) Research focuses on ocean climate dynamics, coastal systems, and solutions to climate challenges. Key areas include Large-scale ocean and climate modeling, Impacts of climate variability on marine ecosystems, Social-ecological-environmental systems, Coastal resilience strategies. He leads initiatives like the Ocean Vital Signs Network and the Global Ecosystem for Ocean Solutions (GEOS), fostering international collaboration. Publications emphasize marine heatwaves, Pacific decadal variability, and coastal flooding. Awards include the PICES SB Award (2013) and Georgia Tech’s Class of 1964 Teaching Award (2012). He advises over 36 Ph.D. students in ocean science programs and co-founded the OCE Data Science High School Internship to advance STEM equity. Labs/Teams: Leads the Di Lorenzo Research Group and collaborates with Woods Hole Oceanographic Institution. Current efforts prioritize equitable ocean solutions, coastal inundation modeling, and climate adaptation frameworks.
Zhen Ming (Jack) Jiang is an Associate Professor and York Research Chair (Tier II) in Software Engineering for Foundation Model-Powered Systems at York University's Department of Electrical Engineering and Computer Science. His research bridges software engineering, artificial intelligence, and computer systems with significant industrial impact. Dr. Jiang earned his Ph.D. from Queen's University's School of Computing and MMath/BMath degrees from the University of Waterloo's David R. Cheriton School of Computer Science. During his doctoral studies, he collaborated with BlackBerry's Performance Engineering team, developing tools now used daily to monitor commercial software systems. His research focuses on engineering rigor for AI-powered applications , software engineering evolution in the Generative AI era , and performance optimization of large-scale systems . Key areas include software performance engineering, mining software repositories, debugging distributed systems, source code analysis, and software visualization. His work combines empirical studies with practical tool development. Recent publications reveal strong trends in applying AI to software engineering challenges, particularly in machine learning systems reliability, blockchain efficiency, and AIOps solutions. His research consistently emphasizes empirical validation using real-world systems and industrial case studies. Scientific recognition includes: York Research Chair (Tier II) in Software Engineering for Foundation Model-Powered Systems NSERC Discovery Accelerator Supplements (DAS), 2020 Best Paper Award at ICST 2016 IEEE Software Best SEIP Paper at ICSE 2015 Ph.D. Research Achievement Award at Queen's University Multiple best paper awards at WCRE, MSR, and ICSE Dr. Jiang actively supervises graduate students and has secured competitive research funding including NSERC grants. His service includes program committee roles for top conferences (ICSE, ASE, ICSME) and editorial work for leading journals (TSE, TOSEM, EMSE). He leads research initiatives focused on foundation model-powered systems, collaborating with industry partners on performance monitoring and debugging solutions for large-scale distributed environments.
Dr. Jason D. Bakos is a Professor in the Department of Computer Science and Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing. His research focuses on high-performance domain-specific architectures, including reconfigurable computing, embedded systems, and machine learning acceleration. He has held academic positions since 2005, progressing from Assistant to Associate Professor before becoming a full Professor in 2017. Education : Ph.D., Computer Science, University of Pittsburgh (2005) B.S., Computer Science, Youngstown State University (1999) Research Interests : Dr. Bakos specializes in computer architecture at multiple levels (circuit, micro-architectural, and system) with a focus on VLSI design, reconfigurable computing, high-performance computing, and applications in embedded systems. His recent work includes FPGA acceleration of machine learning algorithms, structural health monitoring systems, and real-time signal processing. Awards : 2018 Teaching Award in Computer Science and Engineering 2009 NSF CAREER Award Multiple design competition awards for innovative chip and circuit designs Grants & Funding : He leads and co-leads projects funded by NSF, Savannah River National Laboratory, and industry partners like Texas Instruments. Recent grants focus on edge computing for real-time machine learning, FPGA-based accelerators, and corrosion analysis of nuclear materials. Labs & Teams : His research group collaborates on projects involving embedded systems, FPGA design, and interdisciplinary applications in structural engineering and bioinformatics. He advises a dynamic team of graduate students and post-doctoral researchers.
Sujan Kumar Roy is an Assistant Teaching Professor in the Department of Computer Science at Michigan Technological University. He holds a Ph.D. in Machine Learning with Computer Engineering and Signal Processing from Griffith University, Australia, and has earned multiple academic distinctions, including the 'Award of Excellence in the Research Thesis' and consideration for the 'Chancellor's Medal for Excellence in Ph.D. Thesis 2021.' Dr. Roy's teaching focuses on Computational Intelligence, Foundations of Data Science, Machine Learning, Data Mining, and Introduction to Data Science. His research explores applications of AI, ML, and Data Science in Cybersecurity, Medical Image Analysis, Healthcare Systems, and Speech Enhancement. He has contributed extensively to speech enhancement techniques, integrating Kalman filters with machine learning and deep learning approaches. Recent research trends in his publications emphasize the development of robust algorithms for speech enhancement in noisy environments, fusion datasets for hate speech detection, and adaptive filtering methods. His work bridges signal processing and AI, aiming to improve real-time system performance and noise robustness. Awards: Award of Excellence in the Research Thesis Consideration for Chancellor's Medal for Excellence in Ph.D. Thesis 2021 In teaching, Dr. Roy emphasizes foundational concepts and their practical applications. His grants and collaborations focus on advancing AI-driven solutions for healthcare and cybersecurity challenges. He is affiliated with the Department of Computer Science at Michigan Tech, contributing to both academic and research missions.
Travis Wiens is an Associate Professor in the Department of Mechanical Engineering at the University of Saskatchewan. His research focuses on fluid power systems, acoustic sensing, dynamic modeling, mining mechatronics, and artificial intelligence applications in engineering. He holds a B.Sc., M.Sc., and Ph.D. in Mechanical Engineering from the same institution, completed in 2002, 2004, and 2008 respectively. His research interests emphasize innovative solutions for hydraulic control systems, pipeline resonance reduction, and mining safety through acoustic technologies. He has developed low-cost electrohydrostatic actuators and explored additive manufacturing in valve design. Notable contributions include vibrational data communication tools for extractive industries and machine learning models for mine roof stability assessment. Dr. Wiens teaches ENGC 01A - Introduction to Fluid Power Components . His work bridges theoretical fluid dynamics with practical applications in energy efficiency, structural health monitoring, and biomimetic systems. Ongoing research trends include integrating AI with hydraulic systems and advancing non-destructive testing methods for infrastructure integrity. His publications highlight interdisciplinary approaches, combining mechanical engineering principles with computational modeling, neural networks, and mechatronics. Collaborative efforts focus on mining safety, oil and gas infrastructure, and sustainable hydraulic technologies.
